Team4 ROBOT Dexterity

The researchers first had humans wearing motion capture equipment perform a variety of simulated everyday tasks, like stacking cups, opening containers and pouring out the contents, and picking up items with other things balanced on top. All this data — where the hands go, how they interact and so on — was chewed up and ruminated on by a machine learning system, which found that people tended to do one of four things with their hands:

Self-handover: This is where you pick up an object and put it in the other hand so it’s easier to put it where it’s going, or to free up the first hand to do something else. One hand fixed: An object is held steady by one hand providing a strong, rigid grip, while the other performs an operation on it like removing a lid or stirring the contents. Fixed offset: Both hands work together to pick something up and rotate or move it.

One hand seeking: Not actually a two-handed action, but the principle of deliberately keeping one hand out of action while the other finds the object required or performs its own task. The robot put this knowledge to work not in doing the actions itself — again, these are extremely complex motions that current AIs are incapable of executing — but in its interpretations of movements made by a human controller.